Flood risk assessment for sustainable urban development : Case study of Marikina-Pasig-San

Juan river basin, Manila

Mohamed KEFI , PhDDr. Binaya Kumar MISHRA, Dr. Yoshifumi MASAGO

International Conference in Urban and Regional Planning"Planning towards Sustainability and Resilience"

14 – 15 March, 2018 Manila, Philippines

March 14th, 2018

Water and Urban Initiative project (WUI)

• Contribute to evidence-based policymaking for sustainable water environments by assessing their values in Asian cities

◦ Provide scientific tools to forecast the future state of urban water environments

◦ Support capacity development aiming at improving urban water environments

• August 2014 – March 2018

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Project period

Overall objectives

Future Projections1. Flood inundation 2. Urban water quality 3. Flood-related infectious

diseases

Economic evaluation1. Flood damage2. Value of improving

water quality3. Low carbon technologies

in WWTPs

Policy Recommendations1. Flood control2. Wastewater management

Research components

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Background : Global Natural Disaster (1)

2016 : 750 natural events and USD 175 Bn

Events Overall Losses

(Munich RE, 2017)

Hydrological disasters include flood and landsides. This group is the most important class of natural disaster

32% 50%

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Background : Global Natural Disaster (2)

Events Overall Losses

(Munich RE, 2017)

Asian regions are considered as the most exposed

areas in the world to natural disasters

- Frequency and intensity of natural disasters such as flood events areincreasing

- Unplanned Land use and climate change are the main drivers to therise of flood events

- Increasing flood events can conduct to heavy damages with negativesocial and economic impact

- Asian Regions such as the Philippines are considered as the mostexposed areas in the word to flood hazard

- Typhon Ondoy hit many regions (Luzon Island) in September 2009.4.75 Million persons were affected and more than 155,000 houseswere damaged in totally or partially (NDCC, 2009)

Background : Flood

(Ondoy 2009, Marikina DRRM) (Ondoy 2009, Marikina DRRM) (Ondoy 2009, LLDA)

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ObjectivesTo explore feasible options for flood risk managementtowards improving urban water environment of Manila

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• Establishment of flood models (hydrologic and hydraulic) for futureflood assessment in Manila

• Scenarios development for climate and landuse changeconsiderations and alternative countermeasures

• Evaluate the future tangible flood damages in 2030 using spatialanalysis approach

Overview of the methodology

• Flood risk assessment Components

Tools

Parameters

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Hazard Exposure Vulnerability

Flood water depth based on Return

Period

Asset exposed/Element at

risk value

Susceptibility of the exposed assets at

contact with water

Flood simulation (FLO-2D)

Land Use map/Replacememt

cost

Flood damage functions

Flood Damage assessment

Inundated areas/Flood

Depth

Overlay Flood Damage map at

grid levelAffected LULC

Land Use Land Cover(LULC)

2015 - 2030

Flood Damage rate for built-up

(Depth damage function)

Calibration/Validation

Scenarios/Future

Assessment

Total Damage per grid =Damage rate X Affected area

X Unit property value

FLO-2D SoftwareLocal property value

data

LANDSAT

Hypothesis :

1- Assessment of flood damage at Meso-scale 2- Built-up class is applied as an aggregated land use categories3- Property value is assumed to be the replacement cost of degraded

assets

Raster-based GIS approach

-Climate Change (RCPs and GCMs)-Topography (DEM)-Soil type -Land Use Change using Land Change Modeler (LCM) and LANDSAT products-Stromwater Infrastructure

Data

Land Use Classification/LCM

Comp.1

Comp.2

Comp.3 Grid size : 100 m Flood simulation : 100 years return period

Study Area9

Marikina-Pasig-San Juan River system

Inundation modeling area 334 Km2

Hydrologic modeling area 401 Km2

Modeling Approach10

1- Hazard

Inundation modeling : Model set up

Inundation model set up at FLO-2D platform

• Ondoy flood event was used forcalibration of the flood inundationmodel

• Peak discharge at St Nino for 2009flood was about 3500 m3/s(considering upstream inundation).Simulated flood was 3413 m3/s

• Damage analysis

1- Hazard

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• Landsat 7 ETM

• 03/04/2002

• Supervised Classification

Land Use 2002

• Landsat 8 OLI/TIRS

• 07/02/2014

• Supervised Classification

Land Use 2014• Land Change

Modeler (LCM)

• Factors : Elevation/Slope

Land Use 2030

Land Use projection2- Exposure

Projection of future land use based on past data using Land Change Modeler (LCM)

• Flood damage Depth function is graphical relationships of the lossesexpected at a specified depth of flood water

• Physical damage : Flood depth function derived from field survey

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Flood Damage function 3-Vulnerability

Data collection from Barangays

Scenarios Analysis

Current situation

Scenario 1 : Business as UsualClimate and land use

change

Scenario 2 :With Mitigation Countermeasures

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(Average Daily rainfall during Ondoy typhoon (2009) estimated to 356.8 mm)

Daily maximum rainfall for current and future climate (2020-2044)

Current MRI MIROC MRI MIROC

50 322.0 370.1 375.1 402.4 451.0

100 360.8 411.6 425.9 449.6 516.5

RCP85RCP 45Return period

(Year)

Climate Change

Countermeasures: Dam (75 MCM), greater flow capacity (600 to 1200 m3/s of Pasig river), infiltration measures and flood canal

diversion (with full capacity 2400 m3/s instead of 1600 m3/s

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Current

Business-as-Usual

With Mitigation

Result : Comparison of flood inundations

1- Hazard

Montalban for current and future conditions

30% increase of Peak Discharge

Manila City

Pasig City/Taytay

Comparison of flood inundations

Comparison of current and future conditions pointed out an increase of 94% in Business as usual scenario and a reduction with 47% with the implementation of

specific measures

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1- Hazard

+94%

-47%

Land use land cover (LULC) change

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2014 2030

Result : LULCC analysis 18

Urban area will increase Exposure to floods will increase

2- ExposureUrban will Increase

by 10%

Result : LULCC by City19

Met

ro-M

anila

Riz

al

Urbanization of some cities of Rizal in Future

2- Exposure

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Nonlinear regression*

(*XLSTAT)

Flood Damage function

R² = 0.980

3-Vulnerability

𝑫𝒂𝒎𝒂𝒈𝒆 𝒓𝒂𝒕𝒆(%) =𝟏

(𝟏 + 𝑬𝒙𝒑(+𝟒. 𝟖𝟗𝟒 − 𝟏. 𝟕𝟑𝟓 ∗ 𝑫𝒆𝒑𝒕𝒉)

Logistic model (2 parameters)

Flood depth function for built-up

Flood Damage Assessment 21

81 Millions USD

Legend

Flood Damage per grid

No Damage

< 10,000

10,000 - 25,000

25,000 - 50,000

> 50,000

Current Situation(2015)

Business-as-usual (2030)

Mitigation (2030)

2/ Total damage in Rodriguez andSan Mateo will be significant in thefuture

3/ Serious damages along Marikinaand San Juan river

2

1

1/ The damage is important inManila and Pasig City

3

Total Damage : 212%

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Flood Damage vs Flood HazardDamage related

to Depth

Damage related to urbanization

Damage along river

Low risk

Flood measures will contribute to reduce the impact of flood damage.

Low protection

• The climate change projections revealed an increase of 25%and in 100-yrs daily maximum precipitation ;

• The effect of the projected climate change in 2030 canincrease peak discharge by 30% at Montalban from4000 m3/s to 5300 m3/s for 100-yrs return period ;

• The climate scenario reveals : 94% increase in inundationarea for 100 years return period (> 1.5 Depth). Flooddamage will increase by 212% ;

• The implementation of combined flood risk reduction willdecrease flood damage by 35% comparing to currentsituation ;

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Conclusion

Recommendations (1)

1-Marikina-Pasig-San Juan rivers runs through cities whichwill increase their susceptibility to flood such as Marikina city

Revitalization of the river can be a solution to avoid risk topopulation and buildings

2-Manila, Pasig, Taytay and Cainta are prone to floods

Hard and soft flood measures will contribute to reduceflood hazard

3-Flood damage will increase in Rodriguez and San Mateo.

It is mainly due to urbanization which will increase thevulnerability of these cities

Effective urban resilience strategies should be adopted

4- More attention to San Juan river

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Recommendations (2)

• High resolution simulations, more accurate land use,additional GCMs/RCPs are expected to increase theaccuracy of the results of flood hazard ;

• Improvement of flood databases can conduct to reduce thedegree of uncertainty ;

• Flood damage can be more accurate with the use of high-resolution satellite images and the establishment of flooddepth function for different land use type ;

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Contribution to SDGs26

Target 11.3: Enhance inclusive and sustainable urbanizationTarget 11.5: Significantly reduce the number of deaths and peopleaffected and direct economic losses caused by water-relateddisasters

Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters

Target 13.2: Integrate climate change measures into national policies,strategies and planning

Target 1.5: Build the resilience of the poor and those in vulnerablesituations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social andenvironmental shocks and disaster

Thank you!